Full-Dimensional Imaging and Camera Intelligence

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An ordinary camera captures the real 3D scene as a projected 2D image with three channels. During this projection process, some scene information gets lost such as depth and surface normal, and some information gets compressed such as resolution and dynamic range. This presentation answers the questions what information has been lost and how to get them back by introducing the full-dimensional imaging framework. The lost and compressed information is restored as additional channels of a generalized image, e.g., a high-quality depth map with as high resolution as the 2D image, with state of the art computer vision algorithms, computational photography techniques, and novel camera prototyping. The concept of camera intelligence is then introduced to inspire the future research that complements artificial intelligence with camera design for solving visual computing problems that can never be solved using a conventional camera.